{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 图像处理大作业\n",
    "\n",
    "> 这里的主流内容只针对单独的题解，没有写成现成对应处理图像处理的软件；\n",
    "> 但是，在文章的结尾也会给出使用python写出的对应的GUI图像化界面；\n",
    "\n",
    "+ 文章的结构：\n",
    "    + 题目解释及分析;\n",
    "    + 源代码展示及讲解(Python版和MatLab版);\n",
    "    + GUI图形化界面版;"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 题目结构及分析\n",
    "\n",
    "> 这个部分的内容是对题目进行分析,并且观测对应的图像处理的方式，是否能够还原出最终的图像;\n",
    "> 基于这样的目的，下面开始对应的分析思路;\n",
    "\n",
    "\n",
    "+ 此部分结构：\n",
    "    + 1）题目描述\n",
    "    + 2）分步分析并解释对应的图像处理方法\n",
    "    + 3）检验各个方法处理的合理性\n",
    "\n",
    "##### 1）题目描述\n",
    "以医学成像方法OCT技术的实际样品图像为例，由多副同一测量区域的OCT B-scan 光谱图像，恢复样品结构信号，并提取特殊组织结构。\n",
    "1. 图像恢复：\n",
    "    OCT通过测量由成像系统收集到的返回光的光谱，恢复出样品沿深度方向的结构信号。已知光谱信号是沿深度方向的结构信号的傅里叶变换的实部。由光谱数据恢复结构信息。\n",
    "\n",
    "2. 图像优化：\n",
    "由于在同一区域多次测量，可以综合多副图像信息，提高图像信噪比。\n",
    "提示：生物样品由于存在生命活动，不同时刻的图像之间存在移动，注意校正。\n",
    "\n",
    "3. 图像分割：\n",
    "识别特定结构：皮肤表面，血管\n",
    "\n",
    "原始数据为2048*820*4的数据，其中，2048像素对应的方向为光谱信号（即2048*1*1为沿深度方向结构信号的傅里叶变换），820像素对应的方向为成像位置扫描的方向。同一扫描位置扫4遍，共4张图。\n",
    "\n",
    "##### 2) 分步分析并解释对应的图像处理方法\n",
    "\n",
    "最终给出的数据是一个spect.mat文件；mat文件是matlab中保存数据，的一种文件格式;如果我们使用matlab的话，那么非常简单，我们只需要使用一个`variable = load(path);`的指令即可；但是对于python而言，无法直接的打卡`.mat`文件，所以我们需要导入一个包来解决这件事;\n",
    "```python3\n",
    "import scipy.io as scio\n",
    "datapath = \"./spect.mat\"\n",
    "data = scio.loadmat(datapath)\n",
    "```\n",
    "这个datapath作为数据文件存在的路径,然后使用科学计算的包；来进行导入，这个包也支持使用傅立叶变换；也方便后面我们对他的使用；\n",
    "我们一会可以在`.ipynb`中开进行验证一下,如果没有安装过`scipy`的包的话还得使用`pip install scipy`先进行安装;\n",
    "我们通过下面的代码来查看数据;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['__header__', '__version__', '__globals__', 'spect'])\n",
      "(2048, 820, 4)\n"
     ]
    }
   ],
   "source": [
    "import scipy.io as scio\n",
    "datapath = \"./spect.mat\"\n",
    "data = scio.loadmat(datapath)\n",
    "# 查看data中的所有的键;\n",
    "print(data.keys())      # 输出结果: dict_keys(['__header__', '__version__', '__globals__', 'spect'])\n",
    "# 把对应的spect键中的值复值给DataSpect;\n",
    "DataSpect = data['spect']   \n",
    "# 查看数据的维度;\n",
    "print(DataSpect.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过上面的内容，我们可以发现对应的数据是一个三维的数据，它的第三个维度表示的图片的数量；我们使用opencv来对图像进行显示；通过如下的代码;我们可以最终得到的图像如下：\n",
    "\n",
    "<img src=\"./rspect.png\" width = 20%>  <img src=\"./gspect.png\" width = 20%> <img src=\"./bspect.png\" width = 20%> <img src=\"./dspect.png\" width = 20%>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2 as cv\n",
    "r,g,b,d = cv.split(DataSpect)\n",
    "h,w = r.shape[:2]\n",
    "\n",
    "cv.namedWindow(\"r\")\n",
    "cv.imshow('r',r)\n",
    "cv.imshow('g',g)\n",
    "cv.imshow('b',b)\n",
    "cv.imshow('d',d)\n",
    "cv.waitKey()\n",
    "cv.destroyAllWindows()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里显示的图像就是我们的光谱图,我们现在面临的第一个问题就是,如何将光谱图转换为结构图?\n",
    "我们现在重回题目，看看光谱图和结构图之间的关系：\n",
    "\n",
    "> OCT通过测量由成像系统收集到的返回光的光谱，恢复出样品沿深度方向的结构信号。已知光谱信号是沿深度方向\n",
    "> 的结构信号的傅里叶变换的实部。由光谱数据恢复结构信息\n",
    "\n",
    "这里我们可以得出：\n",
    "$$\n",
    "    SpectrumSingle \\rightarrow StructSingle \\\\\n",
    "    SpectrumSingle -F-> SpectrumSingle(i) -real-> StructSingle\n",
    "$$\n",
    "下面我们根据数学公式来推到恢复的过程：\n",
    "\n",
    "$$\n",
    "假设：SpectrumSingle的函数为f(t)        \\\\\n",
    "\\\\\n",
    "    f(t) -傅立叶变换-> F(\\omega)        \\\\\n",
    "    F(\\omega) = \\int_{-\\infty}^{\\infty} f(t)e^{-j\\omega t}dt \\\\\n",
    "\\\\\n",
    "由于，最后的StructSingle只是F(\\omega)的实部，所以StructSingle :\\\\\n",
    "    StructSingle = \\int_{-\\infty}^{\\infty} f(t)\\cos(-\\omega t)dt\\\\\n",
    "$$\n",
    "现在，我们思考，如何使用StructSingle来恢复出$$f(t)$$的值：\n",
    "$$\n",
    "f(t) = \\frac{1}{2\\pi} \\int_{-\\infty}^{\\infty}F(\\omega)e^{j\\omega t}d\\omega                             \\\\\n",
    "如果，把StructSingle当作F(\\omega)带入,得到下面的结果:                                       \\\\\n",
    "\\int_{-\\infty}^{\\infty}(StructSingle)e^{j\\omega t}d\\omega                               \\\\\n",
    "\\int_{-\\infty}^{\\infty}[\\frac{1}{2\\pi} \\int_{-\\infty}^{\\infty} f(t)\\cos(-\\omega t)dt]e^{j\\omega t}d\\omega        \\\\\n",
    "\n",
    "$$"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的公式已经非常的接近我们的结果了，但是如何准确的得出上述公式和我们对应的$f(t)$之间的关系呢？\n",
    "现在困扰我们的就是$cos(\\omega t)$的部分；我么是否可以将他化简？由以下的公式我们可以得到:\n",
    "$$\n",
    "cos(\\omega t) = \\frac{1}{2}(e^{j\\omega t} + e^{-j\\omega t})         \\\\\n",
    "从而上述式子化简为：\n",
    "result(t) = \\frac{1}{4\\pi}\\int_{-\\infty}^{\\infty}[\\int_{-\\infty}^{\\infty}f(t)(e^{j\\omega t} + e^{-j\\omega t})dt]e^{j\\omega t}d\\omega    \\\\\n",
    "\n",
    "result(t) = \\frac{1}{4\\pi}\\int_{-\\infty}^{\\infty}[\\int_{-\\infty}^{\\infty}f(t)(e^{j\\omega t})dt]e^{j\\omega t}d\\omega + \\frac{1}{4\\pi}\\int_{-\\infty}^{\\infty}[\\int_{-\\infty}^{\\infty}f(t)(e^{-j\\omega t})dt]e^{j\\omega t}d\\omega       \\\\\n",
    "$$"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面的式子，我们不难发现,$\\int_{-\\infty}^{\\infty}f(t)e^{-j\\omega t}dt$是对$f(t)$进行傅立叶变换得到$F(\\omega)$\n",
    "\n",
    "而我们的傅立叶逆变换便是$\\frac{1}{2\\pi}\\int_{-\\infty}^{\\infty}F(\\omega)e^{j\\omega t}d\\omega$\n",
    "\n",
    "所以，我们的第二项$\\frac{1}{4\\pi}\\int_{-\\infty}^{\\infty}[\\int_{-\\infty}^{\\infty}f(t)(e^{-j\\omega t})dt]e^{j\\omega t}d\\omega$\n",
    "\n",
    "便是将f(t)先进行傅立叶变换然后再进行逆变换，之后在将结果除2；\n",
    "\n",
    "那我们的第一个等式是等于什么呢？我们继续推导:\n",
    "$$\n",
    "\\frac{1}{4\\pi}\\int_{-\\infty}^{\\infty}[\\int_{-\\infty}^{\\infty}f(t)(e^{j\\omega t})dt]e^{j\\omega t}d\\omega 和f(t)之间的关系是什么呢？\\\\\n",
    "\n",
    "这个也是可以使用我们数学上及其简单的一种方法来进行推导的;\\\\\n",
    "\n",
    "即，另t = -m;则幻化开始:\n",
    "$$"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\int_{-\\infty}^{\\infty}f(-m)e^{-j\\omega m}d(-m) = -\\int_{-\\infty}^{\\infty}f(-m)e^{-j\\omega m}d(m)   \\\\\n",
    "这一步，我们可以获得第一步的结论：  \\\\\n",
    "\\int_{-\\infty}^{\\infty}f(t)(e^{j\\omega t})dt 得到的是一个与f(t)关于原点对称的一个图形的傅立叶变换;\\\\\n",
    "紧接着，外层函数便是对应的傅立叶逆变换;   \\\\\n",
    "所以，最终我们得出结论了:\n",
    "\n",
    "$$"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "StructSingle -傅立叶逆变换-> \\frac{1}{2}StructSingle + \\frac{1}{2}(StructSingle)^{-1}\n",
    "$$\n",
    "用语言来描述的话,就是一个函数经过傅立叶变换之后，只取他的实部，再进行逆变换，我们可以得到他的原图像在加上一个关于原图像中心对称的两个图像的叠加;并且，已经从公式上已经验证;"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3）检验方法处理的合理性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#   我们通过上述推到的数学公式来进行验证:\n",
    "import numpy as np\n",
    "import cv2 as cv\n",
    "import matplotlib.pyplot as plt\n",
    "picture = cv.imread(\"./WechatIMG46173.jpeg\")\n",
    "r,g,b = cv.split(picture)\n",
    "picture_fft = np.fft.fft2(r)\n",
    "picture_fft_real = picture_fft.real\n",
    "picture_struct =np.fft.ifft2(picture_fft_real)\n",
    "plt.subplot(221)\n",
    "plt.imshow(picture_struct.real)\n",
    "plt.subplot(222)\n",
    "plt.imshow(picture)\n",
    "# plt.imshow(20*SobelResult)\n",
    "# plt.imshow(np.log(np.abs(rMove + gMove + bMove + dMove - 4*rMove)))\n",
    "# plt.imshow(DataSpect)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这张图可以说明我们的结论和推到是真确的,下面便开始我们的还原操作吧,我会助益的解释每一行代码(`python`)版，相应的，也会给出对应的`matlab`版的代码:\n",
    "通过对题目的解读，我们发现我们需要对四张图片进行操做,这样太多的相同的动作我们放在函数中进行:\n",
    "\n",
    "```python\n",
    "def operation(image):\n",
    "    image = image.T\n",
    "    for i in range(820):\n",
    "        image[i] =(np.abs(np.fft.ifft(image[i])))\n",
    "    image = image.T\n",
    "    return image\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 源代码展示及讲解(Python版和MatLab版);\n",
    "\n",
    "+ 本章结构：\n",
    "  + 源代码展示\n",
    "  + 细节讲解\n",
    "  + 展示最后的结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1）源代码展示\n",
    "\n",
    "python版：\n",
    "```python\n",
    "import numpy as np\n",
    "import cv2 as cv\n",
    "import scipy.io as scio\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def operation(image):\n",
    "    image = image.T\n",
    "    for i in range(820):\n",
    "        image[i] =(np.abs(np.fft.ifft(image[i])))\n",
    "    image = image.T\n",
    "    return image\n",
    "\n",
    "\n",
    "\n",
    "# 数据图像提取的操作;\n",
    "datapath= \"./spect.mat\"\n",
    "\n",
    "data = scio.loadmat(datapath)\n",
    "# print(data.keys())\n",
    "# 最后的输出的结果为:\n",
    "# dict_keys(['__header__', '__version__', '__globals__', 'spect'])\n",
    "DataSpect = data['spect']\n",
    "# print(DataSpect)\n",
    "\n",
    "# 使用矩阵进行仿射变换的过程;\n",
    "r,g,b,d = cv.split(DataSpect)\n",
    "h,w = r.shape[:2]\n",
    "\n",
    "# 进行仿射变换中的平移变换；\n",
    "MOne = np.float32([[1,0,0],[0,1,0]])\n",
    "MTwo = np.float32([[1,0,-3],[0,1,0]])\n",
    "MThree = np.float32([[1,0,-6],[0,1,0]])\n",
    "MFour = np.float32([[1,0,-9],[0,1,0]])\n",
    "\n",
    "\n",
    "\n",
    "# 下面使用Cv的warpAffine(img,M,(w,h))来进行平移的操作;\n",
    "rMove = cv.warpAffine(r,MOne,(w,h))\n",
    "gMove = cv.warpAffine(g,MTwo,(w,h))\n",
    "bMove = cv.warpAffine(b,MThree,(w,h))\n",
    "dMove = cv.warpAffine(d,MFour,(w,h))\n",
    "\n",
    "# 进行傅立叶变换;\n",
    "rMove = operation(rMove)\n",
    "gMove = operation(gMove)\n",
    "bMove = operation(bMove)\n",
    "dMove = operation(dMove)\n",
    "\n",
    "# 卷积内核;\n",
    "fil = np.ones((3, 2))\n",
    "\n",
    "\n",
    "rMove= cv.filter2D(rMove,-1,fil)\n",
    "gMove= cv.filter2D(gMove,-1,fil)\n",
    "bMove= cv.filter2D(bMove,-1,fil)\n",
    "dMove= cv.filter2D(dMove,-1,fil)\n",
    "\n",
    "\n",
    "# 使用np.log()进行运算;\n",
    "# result = (np.abs(rMove) + np.abs(gMove) + np.abs(bMove) + np.abs(dMove))/4\n",
    "# result = cv.merge((rMove,gMove,bMove))\n",
    "# rMove = cv.applyColorMap(rMove,cv.COLORMAP_AUTUMN)\n",
    "\n",
    "# rresult = np.ones((1024,820,4))\n",
    "# for i in range(1024):\n",
    "#    rresult[i] = result[i]\n",
    "\n",
    "# 进行sobel蒜子进行运算:\n",
    "# SobelResult = cv.Sobel(rresult,cv.CV_64F,0,1)\n",
    "\n",
    "\n",
    "# 进行闭运算的操作:\n",
    "# k = np.ones((5,5),np.uint8)\n",
    "# img_dilate = cv.dilate(rresult,k)\n",
    "# k = np.ones((2,5),np.uint8)\n",
    "# img_erode = cv.erode(img_dilate,k)\n",
    "\n",
    "\n",
    "# 在提取一次边缘:\n",
    "# SobelResult = cv.Sobel(rresult,cv.CV_64F,1,0)\n",
    "\n",
    "\n",
    "# 尝试来提取血管信息:\n",
    "# r,g,b,d = cv.split(rresult)\n",
    "\n",
    "# cv.imshow(\"result\",20*rresult)\n",
    "\n",
    "# 这几张图提取出表层的皮肤是完全没有问题的;\n",
    "# cv.imshow(\"Sobel\",20*SobelResult)\n",
    "# cv.imshow(\"img_erode\",img_erode)\n",
    "# cv.imshow(\"img_dilate\",img_dilate)\n",
    "# cv.imshow(\"r\",rMove)\n",
    "# cv.imshow(\"g\",20*gMove)\n",
    "# cv.imshow(\"b\",20*bMove)\n",
    "# cv.imshow(\"d\",20*dMove)\n",
    "\n",
    "\n",
    "#   最后一个实验的图最好不要用cv.imshow()来进行实现;\n",
    "# cv.imshow(\"test\",20*((rMove - gMove)*(rMove - gMove)/((rMove + gMove)*(rMove + gMove))))\n",
    "# cv.waitKey()\n",
    "# cv.destroyAllWindows()\n",
    "\n",
    "pre_picture = np.log((rMove - gMove)*(rMove - gMove)/((rMove + gMove)*(rMove + gMove)))\n",
    "ord_picture = np.log((bMove - dMove)*(bMove - dMove)/((bMove + dMove)*(bMove + dMove)))\n",
    "mid_picture = (pre_picture + ord_picture)/2\n",
    "# print(result[1023])\n",
    "plt.subplot(221)\n",
    "plt.imshow(mid_picture)\n",
    "plt.subplot(222)\n",
    "plt.imshow(np.log((bMove - dMove)*(bMove - dMove)/((bMove + dMove)*(bMove + dMove))))\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.imshow(np.log(rMove))\n",
    "plt.subplot(224)\n",
    "plt.imshow(np.log(gMove))\n",
    "# plt.imshow(20*SobelResult)\n",
    "# plt.imshow(np.log(np.abs(rMove + gMove + bMove + dMove - 4*rMove)))\n",
    "# plt.imshow(DataSpect)\n",
    "plt.show()\n",
    "```\n",
    "***\n",
    "matlab版：\n",
    "```matlab\n",
    "BaseMatrix = load(\"./spect.mat\");\n",
    "BaseImage = BaseMatrix.spect;\n",
    "%   这里的步骤是提取出四张结构图;\n",
    "ImageOne = BaseImage(:,:,1);\n",
    "ImageTwo = BaseImage(:,:,2);\n",
    "ImageThree = BaseImage(:,:,3);\n",
    "ImageFour = BaseImage(:,:,4);\n",
    "\n",
    "%   显示这四张图的代码就不列出来了;我把他们放在注释里;\n",
    "%{\n",
    "for i = 1:4\n",
    "subplot(1,4,i)\n",
    "imshow(reshape(BaseImage(:,:,i),2048,820));\n",
    "end\n",
    "%}\n",
    "%   通过每张图的数据可知,这个是每张图都存在一个偏移量;\n",
    "\n",
    "ImageTwo = imtranslate(ImageTwo,[-3,0]);\n",
    "ImageThree = imtranslate(ImageThree,[-6,0]);\n",
    "ImageFour = imtranslate(ImageFour,[-9,0]);\n",
    "%   显示对应的还原图像:\n",
    "\n",
    "for i = 1:820\n",
    "ImageOne(:,i) = ifft(ImageOne(:,i));\n",
    "end\n",
    "for i = 1:820\n",
    "ImageTwo(:,i) = ifft(ImageTwo(:,i));\n",
    "end\n",
    "for i = 1:820\n",
    "ImageThree(:,i) = ifft(ImageThree(:,i));\n",
    "end\n",
    "for i = 1:820\n",
    "ImageFour(:,i) = ifft(ImageFour(:,i));\n",
    "end\n",
    "%   可以大概的看一下对应的复原出来的图像;\n",
    "% imshow(20*abs(ImageOne));\n",
    "\n",
    "%   去除噪音第二题;\n",
    "picture = (ImageOne + ImageTwo + ImageThree + ImageFour)./4;\n",
    "imshow(20*picture)\n",
    "\n",
    "%   第三题的第一小问:进行先开后闭合的运算来提取皮肤;\n",
    "se=strel('square',2);     %采用半径为4的矩形作为结构元素\n",
    "Image_Open = imopen(abs(ImageOne),se);         %开启操作\n",
    "Image_Close=imclose(Image_Open,se);        %闭合操作\n",
    "StructImage = edge(Image_Close,'sobel');\n",
    "% imshow(20*StructImage);                   % 展示边缘检测的成果;\n",
    "structClose = imclose(StructImage,se);\n",
    "structOpen = imopen(structClose,se);\n",
    "% imshow(structOpen)\n",
    "\n",
    "ImageA = (abs(ImageOne) + abs(ImageTwo));\n",
    "ImageB = (abs(ImageOne) - abs(ImageTwo));\n",
    "resultImage = (ImageB.^2) ./ (ImageA.^2);\n",
    "% imshow(resultImage);\n",
    "```\n",
    "\n",
    "##### 2）细节讲解\n",
    "回顾题目给出的条件:\n",
    "\n",
    "\n",
    "\n",
    ">原始题目要求：\n",
    ">\n",
    ">2. 图像优化：\n",
    ">\n",
    ">由于在同一区域多次测量，可以综合多副图像信息，提高图像信噪比。\n",
    ">\n",
    ">提示：生物样品由于存在生命活动，不同时刻的图像之间存在移动，注意校正。\n",
    "\n",
    "\n",
    "\n",
    "我们这里对图片进行位移的矫正，这个很简单，在python中我们可以使用pandas用表格存储，然后自己查看一下，在matlab中，我们直接在工作区打开对应的数据即可我们发现，每张图之间都存在着3个左边位移的变换;所以，直截了当的以脚本的思路来进行编程:\n",
    "\n",
    "```python\n",
    "# 下面使用Cv的warpAffine(img,M,(w,h))来进行平移的操作;\n",
    "rMove = cv.warpAffine(r,MOne,(w,h))\n",
    "gMove = cv.warpAffine(g,MTwo,(w,h))\n",
    "bMove = cv.warpAffine(b,MThree,(w,h))\n",
    "dMove = cv.warpAffine(d,MFour,(w,h))\n",
    "\n",
    "# 进行傅立叶变换;\n",
    "rMove = operation(rMove)\n",
    "gMove = operation(gMove)\n",
    "bMove = operation(bMove)\n",
    "dMove = operation(dMove)\n",
    "```\n",
    "\n",
    "这样一来之前恢复的结构图便可以直接的以同一个坐标位置为起点，而至于后面的提高图像的信噪比，我们可以使用最简单的方法，就是将几张图片叠加，然后除4；就是算他们的均值:\n",
    "\n",
    "看下面的公式:\n",
    "$$\n",
    "resultPicture = \\frac{1}{n}\\sum_{i=0}^{n-1}Picture(i)\n",
    "$$\n",
    "\n",
    "每张图片的噪声是不确定的，如果叠加起来除以除以总数，那么最后的噪声的部分就会越来越小；相反的，有用的信号就会被保留;\n",
    "\n",
    "现在我们再来看看最后一题，也是最难的一个部分;(对信号的提取)\n",
    "\n",
    "> ​\t3、图像分割：\n",
    "> ​\t识别特定结构：皮肤表面，血管\n",
    ">\n",
    "> 原始数据为2048*820*4的数据，其中，2048像素对应的方向为光谱信号（即2048*1*1为沿深度方向结构信号的傅里叶变换），820像素对应的方向为成像位置扫描的方向。同一扫描位置扫4遍，共4张图。\n",
    "\n",
    "+ 皮肤的提取：\n",
    "\n",
    "对于皮肤的提取，有一个很简单的思路就是，先进行`sobel`算子进行一次边缘的提取，然后再通过调节膨胀和腐蚀的矩阵来进行查看最好的边缘检测的效果图;\n",
    "\n",
    "```python\n",
    "rresult = np.ones((1024,820,4))\n",
    "for i in range(1024):\n",
    "   rresult[i] = result[i]\n",
    "\n",
    "# 进行sobel蒜子进行运算:\n",
    "SobelResult = cv.Sobel(rresult,cv.CV_64F,0,1)\n",
    "\n",
    "# 进行闭运算的操作:\n",
    "k = np.ones((5,5),np.uint8)\n",
    "img_dilate = cv.dilate(rresult,k)\n",
    "k = np.ones((2,5),np.uint8)\n",
    "img_erode = cv.erode(img_dilate,k)\n",
    "```\n",
    "\n",
    "+ 血管的提取：\n",
    "\n",
    "  对于血管的提取，我承认，确实是非常难的，在和老师讨论了许久后，才算是出最后的结果图:\n",
    "\n",
    "  总体的思路如下，通过差异化的显示每张图之间的不相关性，通过公式进行检测，看最后的出图效果；\n",
    "\n",
    "  公式如下:\n",
    "  $$\n",
    "  resultPicture = \\frac{(PictureOne-PictureTwo)^2}{(PictureOne+PictureTwo)^2}\n",
    "  $$\n",
    "  \n",
    "\n",
    "这个公式的对于不相关区域的图像的提取，也是和上面的去除噪声的思路很像;但是，他是通过和自身的叠加进行比较，并且为了减弱常系数的作用，使用了平方来达到这样的效果:\n",
    "\n",
    "```python\n",
    "plt.imshow(np.log((bMove - dMove)*(bMove - dMove)/((bMove + dMove)*(bMove + dMove))))\n",
    "plt.show()\n",
    "```\n",
    "\n",
    "\n",
    "##### 3）展示最后的结果\n",
    "\n",
    "这个部分在这里可以直接使用运行按钮运行就好;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/cl/tzgxqdnj4vs4h2vcfwcbgzx80000gn/T/ipykernel_32959/3400125091.py:103: RuntimeWarning: invalid value encountered in divide\n",
      "  pre_picture = np.log((rMove - gMove)*(rMove - gMove)/((rMove + gMove)*(rMove + gMove)))\n",
      "/var/folders/cl/tzgxqdnj4vs4h2vcfwcbgzx80000gn/T/ipykernel_32959/3400125091.py:104: RuntimeWarning: invalid value encountered in divide\n",
      "  ord_picture = np.log((bMove - dMove)*(bMove - dMove)/((bMove + dMove)*(bMove + dMove)))\n",
      "/var/folders/cl/tzgxqdnj4vs4h2vcfwcbgzx80000gn/T/ipykernel_32959/3400125091.py:110: RuntimeWarning: invalid value encountered in divide\n",
      "  plt.imshow(np.log((bMove - dMove)*(bMove - dMove)/((bMove + dMove)*(bMove + dMove))))\n",
      "/var/folders/cl/tzgxqdnj4vs4h2vcfwcbgzx80000gn/T/ipykernel_32959/3400125091.py:113: RuntimeWarning: divide by zero encountered in log\n",
      "  plt.imshow(np.log(rMove))\n",
      "/var/folders/cl/tzgxqdnj4vs4h2vcfwcbgzx80000gn/T/ipykernel_32959/3400125091.py:115: RuntimeWarning: divide by zero encountered in log\n",
      "  plt.imshow(np.log(gMove))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2 as cv\n",
    "import scipy.io as scio\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def operation(image):\n",
    "    image = image.T\n",
    "    for i in range(820):\n",
    "        image[i] =(np.abs(np.fft.ifft(image[i])))\n",
    "    image = image.T\n",
    "    return image\n",
    "\n",
    "\n",
    "\n",
    "# 数据图像提取的操作;\n",
    "datapath= \"./spect.mat\"\n",
    "\n",
    "data = scio.loadmat(datapath)\n",
    "# print(data.keys())\n",
    "# 最后的输出的结果为:\n",
    "# dict_keys(['__header__', '__version__', '__globals__', 'spect'])\n",
    "DataSpect = data['spect']\n",
    "# print(DataSpect)\n",
    "\n",
    "# 使用矩阵进行仿射变换的过程;\n",
    "r,g,b,d = cv.split(DataSpect)\n",
    "h,w = r.shape[:2]\n",
    "\n",
    "# 进行仿射变换中的平移变换；\n",
    "MOne = np.float32([[1,0,0],[0,1,0]])\n",
    "MTwo = np.float32([[1,0,-3],[0,1,0]])\n",
    "MThree = np.float32([[1,0,-6],[0,1,0]])\n",
    "MFour = np.float32([[1,0,-9],[0,1,0]])\n",
    "\n",
    "\n",
    "\n",
    "# 下面使用Cv的warpAffine(img,M,(w,h))来进行平移的操作;\n",
    "rMove = cv.warpAffine(r,MOne,(w,h))\n",
    "gMove = cv.warpAffine(g,MTwo,(w,h))\n",
    "bMove = cv.warpAffine(b,MThree,(w,h))\n",
    "dMove = cv.warpAffine(d,MFour,(w,h))\n",
    "\n",
    "# 进行傅立叶变换;\n",
    "rMove = operation(rMove)\n",
    "gMove = operation(gMove)\n",
    "bMove = operation(bMove)\n",
    "dMove = operation(dMove)\n",
    "\n",
    "# 卷积内核;\n",
    "fil = np.ones((3, 2))\n",
    "\n",
    "\n",
    "rMove= cv.filter2D(rMove,-1,fil)\n",
    "gMove= cv.filter2D(gMove,-1,fil)\n",
    "bMove= cv.filter2D(bMove,-1,fil)\n",
    "dMove= cv.filter2D(dMove,-1,fil)\n",
    "\n",
    "\n",
    "# 使用np.log()进行运算;\n",
    "# result = (np.abs(rMove) + np.abs(gMove) + np.abs(bMove) + np.abs(dMove))/4\n",
    "# result = cv.merge((rMove,gMove,bMove))\n",
    "# rMove = cv.applyColorMap(rMove,cv.COLORMAP_AUTUMN)\n",
    "\n",
    "# rresult = np.ones((1024,820,4))\n",
    "# for i in range(1024):\n",
    "#    rresult[i] = result[i]\n",
    "\n",
    "# 进行sobel蒜子进行运算:\n",
    "# SobelResult = cv.Sobel(rresult,cv.CV_64F,0,1)\n",
    "\n",
    "\n",
    "# 进行闭运算的操作:\n",
    "# k = np.ones((5,5),np.uint8)\n",
    "# img_dilate = cv.dilate(rresult,k)\n",
    "# k = np.ones((2,5),np.uint8)\n",
    "# img_erode = cv.erode(img_dilate,k)\n",
    "\n",
    "\n",
    "# 在提取一次边缘:\n",
    "# SobelResult = cv.Sobel(rresult,cv.CV_64F,1,0)\n",
    "\n",
    "\n",
    "# 尝试来提取血管信息:\n",
    "# r,g,b,d = cv.split(rresult)\n",
    "\n",
    "# cv.imshow(\"result\",20*rresult)\n",
    "\n",
    "# 这几张图提取出表层的皮肤是完全没有问题的;\n",
    "cv.imshow(\"Sobel\",20*SobelResult)\n",
    "cv.imshow(\"img_erode\",img_erode)\n",
    "cv.imshow(\"img_dilate\",img_dilate)\n",
    "cv.imshow(\"r\",rMove)\n",
    "cv.imshow(\"g\",20*gMove)\n",
    "cv.imshow(\"b\",20*bMove)\n",
    "cv.imshow(\"d\",20*dMove)\n",
    "\n",
    "\n",
    "#   最后一个实验的图最好不要用cv.imshow()来进行实现;\n",
    "# cv.imshow(\"test\",20*((rMove - gMove)*(rMove - gMove)/((rMove + gMove)*(rMove + gMove))))\n",
    "cv.waitKey()\n",
    "cv.destroyAllWindows()\n",
    "\n",
    "pre_picture = np.log((rMove - gMove)*(rMove - gMove)/((rMove + gMove)*(rMove + gMove)))\n",
    "ord_picture = np.log((bMove - dMove)*(bMove - dMove)/((bMove + dMove)*(bMove + dMove)))\n",
    "mid_picture = (pre_picture + ord_picture)/2\n",
    "# print(result[1023])\n",
    "\"\"\"\n",
    "结构图和血管;\n",
    "plt.subplot(221)\n",
    "plt.imshow(mid_picture)\n",
    "plt.subplot(222)\n",
    "plt.imshow(np.log((bMove - dMove)*(bMove - dMove)/((bMove + dMove)*(bMove + dMove))))\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.imshow(np.log(rMove))\n",
    "plt.subplot(224)\n",
    "plt.imshow(np.log(gMove))\n",
    "# plt.imshow(20*SobelResult)\n",
    "# plt.imshow(np.log(np.abs(rMove + gMove + bMove + dMove - 4*rMove)))\n",
    "# plt.imshow(DataSpect)\n",
    "plt.show()\n",
    "\"\"\""
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7 (main, Sep 14 2022, 22:38:23) [Clang 14.0.0 (clang-1400.0.29.102)]"
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